Method and system for communication in a wireless network

ABSTRACT

Methods and apparatus are described for processing data in a wireless communication network. Iterative estimation techniques are used to enable tracking of time-varying communication channels. A signal is transmitted over a channel in the network, the signal comprising a sequence of symbols carried on a plurality of sub-carriers. Boot-up estimator estimates, in a time domain, parameters of a model of the channel based on the received signal. A domain converter transforms at least one of the estimated parameters from the time domain to provide at least one transformed parameter in a second domain. An equalizer and decoder determine estimates of symbols from the received signal using the at least one transformed parameter, and tracking estimator updates the estimated model parameters during reception of the signal using at least one estimated symbol.

RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 12/224,457 filed Aug. 26, 2008 as a nationalizationunder 35 U.S.C. 371 of PCT/AU2007/000231, filed Feb. 27, 2007 andpublished as WO 2007/095697 A1 on Aug. 30, 2007, which claimed priorityunder 35 U.S.C. 119 to Australian Patent Application Serial No.2006900984, filed Feb. 27, 2006; which applications and publication areincorporated herein by reference and made a part hereof.

FIELD OF INVENTION

The present invention relates to the field of wireless communications.In particular, the present invention relates to improved multi-carrierwireless communications. In one particular form, the invention relatesto an improved signal processing method and apparatus for a wirelesscommunication system. It will be convenient to hereinafter describe theinvention in relation to the use of a packet based wireless OFDM(Orthogonal Frequency Division Multiplexing) communication system,however, it should be appreciated that the present invention may not belimited to that use.

BACKGROUND

Throughout this specification the use of the word “inventor” in singularform may be taken as reference to one (singular) or more (plural)inventors of the present invention. The inventor has identified thefollowing related art.

In Applicant's co-pending International (PCT) Applications,PCT/AU03/00502 and PCT/2004/001036 both published under WIPO publicationNumbers WO 03/094037 and WO 2005/11128 respectively, a number ofbackground art systems have been identified relating in particular towireless communications systems based on so-called multiple accesstechniques in which information such as voice and data are communicated.The specifications of WO 03/094037 and WO 2005/11128 are incorporatedherein by reference in their entirety.

The inventor has recognised that the performance of a MobileCommunications System may be heavily dependent on the quality of thePhysical Layer (PHY) processing. The PHY may provide for deliveringcoverage and robustness to radio links between nodes that move throughhostile propagation conditions such as those encountered outdoors, andareas of high interference. Mobility, and in particular high speedterrestrial mobility, may induce yet another set of difficulties for thePHY as the reflections off the surrounding buildings, vehicles and otherbodies may combine in a time varying manner.

Some wireless network vendors have incorporated legacy IEEE 802.11 radiotechnologies into their systems. Conventional IEEE 802.11 radios havebeen designed for stationary indoor propagation environments and theiruse in outdoor mobile communications networks may be ill founded from atechnical perspective. Standardization efforts within the IEEE 802.16eand 802.20 Physical Layer working groups may be considered as focused onproviding a waveform for transmission that is more compatible with thecommunications challenges faced while travelling outdoors at speed.Standards typically do not specify how to receive signals, ratherfocussing on what signals should be transmitted. The vendors are thenresponsible for the receiver technology.

The inventor considers that Orthogonal Frequency Division Multiplexing(OFDM) is well suited to broadband wireless communications. However,this technique may have been historically applied to the problem oftransmitting data in a stationary indoor environment. The outdoor urbanenvironment may contain many obstacles for the radio signal, such asbuildings and trees, which are referred to as clutter. Present wirelesstechnology may be able to offer high throughput only at the expense ofreceiver sensitivity, hence the cluttered urban environment may lead topoor coverage. Furthermore, the relative mobility between thetransmitter and receiver may cause the placement of these obstacles tochange in time. When the effects of mobility and clutter combine, theresulting wireless channel may present a significant challenge to thecommunications system designer.

The radio signal in an outdoor communications system may be subject todistortion caused by the propagation environment, i.e. channel, on theradio signal. The channel may distort the transmitted signal by alteringits magnitude and/or phase, potentially resulting in the loss ofinformation. Moreover, relative mobility between the transmitter andreceiver, and/or time varying frequency offset effects, cause thechannel conditions to vary with time.

Signal reflections and diffractions can result in multiple copies of thetransmission being received, i.e. multipath effects. Typically each ofthese multipath components may have been subjected to different effectsupon their phase and magnitude. The discrete time channel impulseresponse, and its associated Power Delay Profile (PDP), represent eachmultipath contribution as a time domain tap. A level of intensity, and aphase rotation, is assigned to each tap in order to represent itscontribution to the overall received signal. The delay spread of thechannel, is the delay between the arrival of the first and lastmultipath contributions in the PDP. The RMS delay spread, which isderived from its PDP, is a single value which accounts for eachmultipath contribution, weighted according to its delay and magnitude. Ahigher RMS delay spread indicates that the channel is likely to have astronger effect on the signal.

The power delay profile for an example indoor wireless channel, basedupon the ETSI BRAN model B [2] is shown in FIG. 1. This channel has anRMS delay spread of 150 ns.

Multipath propagation can lead to an OFDM symbol being subjected tointerference from previously transmitted OFDM symbols, i.e. inter-symbolinterference (ISI). Multipath effects can also degrade the orthogonalityof subcarriers, thus leading to an individual subcarrier being subjectto interference from other subcarriers within the same OFDM symbol, i.e.inter-carrier interference (101). OFDM provides several mechanisms whichare intended to mitigate multipath effects. For example, it is commonfor each OFDM symbol to include a guard interval, which separatessuccessively transmitted OFDM symbols. If the guard interval is selectedto be greater than the delay spread of the channel then ISI and ICIcannot occur. The presence of this guard increases the time taken totransmit each OFDM symbol and thus reduces the data rate of the system,decreases the power efficiency and decreases the spectral efficiency ofthe system.

In order to successfully demodulate the signal, the influence of thechannel is first cancelled and hence an estimate of the channel isrequired. A set of training symbols may be transmitted at the start ofthe packet to be used for channel estimation, which are known to thereceiver. These symbols are referred to as preamble symbols and thetechnique is dictated in several standards, e.g. IEEE 802.11a/g and802.16-2005. A common approach is to estimate the effect of the channelin the frequency domain [2]. To this end, a cyclic prefix 2 is insertedin the guard interval immediately prior to a symbol 4, containing atime-domain copy of the end 6 of the symbol, as shown in FIG. 2.

The presence of a cyclic prefix that is longer than the delay spread ofthe channel permits the assumption that the transmitted OFDM symbolundergoes a cyclic convolution with the channel. In this case, onemethod for calculating the channel response is to simply divide thereceived frequency domain symbol by the known preamble symbol. FIG. 3(a) shows an example of the channel estimate provided using thistechnique in an 802.11a system, for the indoor channel model presentedabove, in the absence of noise on the channel. The 802.11a standardspecifies that each OFDM symbol have a total duration of 4 μs, includinga 0.8 μs cyclic prefix. Moreover, the second long preamble symbol isidentical to the first, providing an effective cyclic prefix of 3.2 μsduration for this symbol. This guard interval is significantly longerthan the typical delay spread of the indoor channel and thus an accuratechannel estimate can usually be obtained.

The coherence bandwidth of a channel is inversely proportional to itsdelay spread. Hence, as the indoor channel in the example above has arelatively short delay spread, a strong correlation between thefrequency domain response of adjacent subcarriers is observed in FIG. 3(a). In cases where the coherence bandwidth is significantly larger thenthe subcarrier spacing, this correlation may be exploited to improve theaccuracy of the estimate, for example by smoothing it across thefrequency domain.

When the length of the delay spread does not exceed the cyclic prefixduration, the effect of the channel may be equalized in the frequencydomain using a one-tap linear equalizer. Once the channel estimate hasbeen obtained, it is used during the equalization process. Againemploying the assumption of a time domain cyclic convolution of thetransmitted data and the channel, the equalized frequency domainobservation can be simply obtained via division of the receivedobservation by the estimated channel response. FIG. 3( b) shows anobservation, after frequency domain equalization, for the example indoorchannel, when using an 802.11a system. Each OFDM data symbol isprotected by a cyclic prefix of duration 0.8 μs, which is greater thanthe delay spread of the indoor channel, and hence no inter-symbol orinter-carrier interference is observed.

The outdoor propagation environment can be significantly more disruptiveto a signal than that experienced indoors. When propagating through theoutdoor urban environment, the radio signal is subject to obstacles suchas buildings, trees and other clutter, which can lead to strongreflective and/or diffractive multipath effects [1]. As a result, thedelay spread of the outdoor wireless channel is typically significantlylarger than that of its indoor counterpart. The power delay profile foran example outdoor wireless channel is shown in FIG. 4. Here the IEEE802.20 Typical Urban (Case-IV) model is employed, having an RMS delayspread of 0.8 μs.

The long delay spread experienced outdoors can present problems forconventional receiver techniques, such as the channel estimation andequalization methods described above. The problems arise if the cyclicprefix length is not sufficient to cover the delay spread of thechannel. One such notable case exists when an OFDM waveform that isdesigned for indoor use, such as the IEEE 802.11a/g waveform, isemployed in an urban outdoor environment. As described above, the secondlong preamble symbol in the 802.11a waveform is a replica of the first,and as such this training symbol is provided with an effective cyclicprefix of length 3.2 μs. Hence, when using the frequency domain channelestimation technique described above, the second long preamble is almostcompletely guarded from ISI and ICI effects being induced by the exampleoutdoor channel. However, the residual channel effects cause a slightvariation of the estimate from the exact channel, as illustrated in FIG.5( a). Moreover, due to the long delay spread experienced outdoors, thecoherence bandwidth of the channel is significantly shorter than that ofthe indoor channel. This may be observed when comparing the relativelylow correlation between adjacent subcarriers in FIG. 5 (a) to the strongcorrelation shown in FIG. 2 (a). Hence the potential to improve theaccuracy of the channel estimate via frequency domain smoothing isreduced for the case of the urban outdoor environment.

In contrast to the heavily guarded preamble, data bearing OFDM symbolsin the 802.11a packet are only afforded a cyclic prefix guard of length0.8 μs. Each multipath component which has a delay exceeding the guardlength will contribute a part of the previously transmitted OFDM symbolinto the received observation, effectively jumping over the guardinterval. As a result, the assumption of a cyclic convolution betweenthe time domain symbol and channel is invalid. The resulting equalizedreceived observation is heavily distorted by ISI and ICI effects, asshown in FIG. 5( b).

The strong multipath effects experienced in an outdoor urbanenvironment, result in a wireless channel which exhibits a high RMSdelay spread. In some cases it may be possible to extend the length ofthe cyclic prefix beyond that of the delay spread. For example, the IEEE802.16 standard provides several options for cyclic prefix length, oneof which may be suitable for the particular radio channel beingemployed. However, an extension of the cyclic prefix results in wastedtransmit energy and decreased spectral efficiency. In situations wheretransmit power is limited, e.g. by a regulatory body, wasted transmitenergy equates to reduced range and/or data rate. In other cases thelength of the cyclic prefix may be fixed, e.g. IEEE 802.11a/g, andconventional receiver techniques may fail under stress of the resultinginter-symbol and inter-carrier interference.

Any discussion of documents, devices, acts or knowledge in thisspecification, either within the text of this specification or, materialincorporated herein by reference is included to explain the context ofthe invention. It should not be taken as an admission that any of thematerial forms a part of the prior art base or the common generalknowledge in the relevant art in Australia, the United States of Americaor elsewhere on or before the priority date of the disclosure and claimsherein.

SUMMARY OF INVENTION

It is an object of the present invention to overcome or mitigate atleast one of the disadvantages of related art systems.

The inventor has recognised that in the case of relative mobility of thetransmitter and receiver, and/or a time varying frequency offset, theconventional assumption that the channel is quasistatic may break downand in order to perform reliable equalization it may be necessary totrack the channel and update the channel estimate throughout theduration of a packet.

According to a first aspect of the invention there is provided a methodof data processing in a wireless communication network, the methodcomprising:

obtaining a model of a channel in the wireless communication network;

receiving a multi-carrier signal transmitted over the channel;

estimating, based on the received signal, parameters of the model of thechannel; and

updating the estimated parameters during reception of the signal.

According to a further aspect of the invention there is provided amethod of data processing in a wireless communication network, themethod comprising:

obtaining a model of a channel in the wireless communication network;

receiving a signal transmitted over the channel, the signal comprising asequence of symbols carried on a plurality of sub-carriers;

estimating, based on the received signal, parameters of the model of thechannel;

determining estimates of symbols from the received signal using at leastone of the estimated model parameters; and

updating the estimated model parameters during reception of the signalusing at least one estimated symbol.

According to a further aspect of the invention there is provided amethod of data processing in a wireless communication network, themethod comprising:

obtaining a model of a channel in the wireless communication network;

receiving a signal transmitted over the channel, the signal comprising asequence of symbols carried on a plurality of sub-carriers;

estimating, based on the received signal, parameters of the model of thechannel, said estimating being performed in a time domain;

transforming at least one of the estimated parameters from the timedomain to provide at least one transformed parameter in a second domain;

determining estimates of symbols from the received signal using the atleast one transformed parameter; and

updating the estimated model parameters during reception of the signalusing at least one estimated symbol.

According to a further aspect of the invention there is provided anapparatus for processing data in a wireless communication network, theapparatus comprising:

at least one signal receiver operable to receive a multi-carrier signaltransmitted over a channel in the network;

a boot-up estimator that estimates parameters of a model of the channelbased on the received signal; and

a tracking estimator that updates the estimated parameters duringreception of the signal.

According to a further aspect of the invention there is provided anapparatus for processing data in a wireless communication network, theapparatus comprising:

at least one signal receiver operable to receive a signal transmittedover a channel in the network, the signal comprising a sequence ofsymbols carried on a plurality of sub-carriers;

a boot-up estimator that estimates parameters of a model of the channelbased on the received signal;

a symbol estimator that determines estimates of symbols from thereceived signal using at least one of the estimated model parameters;and

a tracking estimator that updates the estimated model parameters duringreception of the signal using at least one estimated symbol.

According to a further aspect of the invention there is provided anapparatus for processing data in a wireless communication network, theapparatus comprising:

at least one signal receiver operable to receive a signal transmittedover a channel in the network, the signal comprising a sequence ofsymbols carried on a plurality of sub-carriers;

a boot-up estimator that estimates, in a time domain, parameters of amodel of the channel based on the received signal;

a domain converter that transforms at least one of the estimatedparameters from the time domain to provide at least one transformedparameter in a second domain;

a symbol estimator that determines estimates of symbols from thereceived signal using the at least one transformed parameter; and

a tracking estimator that updates the estimated model parameters duringreception of the signal using at least one estimated symbol.

According to a further aspect of the invention there is provided anapparatus for data processing in a wireless communication network,comprising:

means for obtaining a model of a channel in the wireless communicationnetwork;

means for receiving a multi-carrier signal transmitted over the channel;

means for estimating, based on the received signal, parameters of themodel of the channel; and

means for updating the estimated parameters during reception of thesignal.

According to a further aspect of the invention there is provided anapparatus for data processing in a wireless communication network,comprising:

means for obtaining a model of a channel in the wireless communicationnetwork;

means for receiving a signal transmitted over the channel, the signalcomprising a sequence of symbols carried on a plurality of sub-carriers;

means for estimating, based on the received signal, parameters of themodel of the channel;

means for determining estimates of symbols from the received signalusing at least one of the estimated model parameters; and

means for updating the estimated model parameters during reception ofthe signal using at least one estimated symbol.

According to a further aspect of the invention there is provided anapparatus for data processing in a wireless communication network,comprising:

means for obtaining a model of a channel in the wireless communicationnetwork;

means for receiving a signal transmitted over the channel, the signalcomprising a sequence of symbols carried on a plurality of sub-carriers;

means for estimating, based on the received signal, parameters of themodel of the channel, said estimating being performed in a time domain;

means for transforming at least one of the estimated parameters from thetime domain to provide at least one transformed parameter in a seconddomain;

means for determining estimates of symbols from the received signalusing the at least one transformed parameter; and

means for updating the estimated model parameters during reception ofthe signal using at least one estimated symbol.

According to a further aspect of the invention there is provided acomputer program product comprising machine-readable program coderecorded on a machine-readable recording medium, for controlling theoperation of a data processing apparatus on which the program codeexecutes to perform a method of data processing in a multi-carrierwireless communication network, the method comprising:

obtaining a model of a channel in the wireless communication network;

receiving a multi-carrier signal transmitted over the channel;

estimating, based on the received signal, parameters of the model of thechannel; and

updating the estimated parameters during reception of the signal.

The inventor has also identified that when preambles are employed inpacket waveforms to assist with initial channel estimation, these mayhave autocorrelation properties that can lead to the appearance of falsetaps, where it appears to the channel estimator that there is amultipath reflection of a particular delay, while in fact there is not.Additionally, the inventor recognises that time domain correlation forevery possible tap delay may be expensive in terms of processingrequirements.

In a further aspect the present invention provides a method of selectinga signalling domain channel estimate tap in a wireless multicarriercommunication network including the steps of:

comparing a signal parameter of a plurality of taps to a predeterminedthreshold;

providing signal processing to only those taps that are determined tohave a signal parameter that exceeds the threshold.

In a further aspect the invention provides a method of generating achannel estimate in a wireless multicarrier communication networkcomprising the steps of:

generating a set of windowed convolutional channel estimator matricesbased on at least a channel estimate Mean Squared Error andcorresponding to optimised variables relating to a window of a symbolpacket portion;

selecting one of the matrices based on a metric from a PHY statecalculator;

applying the selected matrix to a received observation.

According to a further aspect, the invention provides method ofgenerating a channel estimate in a wireless multicarrier communicationnetwork comprising the steps of:

generating a set of MMSE channel estimate matrices based on at least achannel estimate Mean Squared Error and corresponding to optimisedvariables relating to a window of a symbol packet portion and determinednoise power;

selecting one of the matrices based on a metric from a PHY statecalculator;

applying the selected matrix by a matrix-vector multiply operation to areceived observation so as to determine a time domain channel estimate.

Preferably the method stores the vectors of the selected matrix for usein subsequent channel estimation process steps, for example in a shiftregister memory.

According to a further aspect, the invention provides a method ofgenerating a local time domain channel estimate in a wirelessmulticarrier communication network comprising the steps of:

providing a current observation sequence;

providing a current data estimate sequence;

providing a data estimate state comprising data estimates of precedingreceived data;

inserting at least one the current data estimates into the data estimatestate;

for at least one predetermined channel delay, correlating the currentobservation with the data estimate state using a window on the dataestimate state offset from a previous symbol in the data estimate stateby an amount substantially equal to the at least one predeterminedchannel delay.

According to a further aspect, the invention provides a method ofgenerating a local time domain channel estimate comprising the steps of:

providing a current observation sequence;

providing a current data estimate sequence;

providing a data estimate state comprising data estimates of precedingreceived data;

inserting at least one the current data estimates into the data estimatestate;

generating a MMSE matrix filter given by, (A*A+σ²I)⁻¹A*, from thewindowed convolution matrix A of the data estimate state and a noisepower hypothesis σ² from a PHY State Calculator;

applying the MMSE matrix to an observation sequence y via aMatrix-Vector multiply to determine a local time domain channel estimategiven by (A*A+σ⁻²I)⁻¹A*y

The method may further comprise

determining an estimate of a transform domain channel according to:{circumflex over (R)}=G _(rx) *ĤG _(tx)

where the matrix {circumflex over (R)}, is an estimate of the matrix Rdefined as herein, which is calculated as above, using the matrix Ĥwhich comprises an estimate of the time varying time domain channelmatrix H defined as herein;

determining a Maximum Ratio Combiner sequence from an observationaccording to:{circumflex over (X)}={circumflex over (R)}*Y

where {circumflex over (R)} is defined as above and {circumflex over(X)} is an estimate of X defined as herein.

In further aspects the described methods may comprise:

determining an estimate of a time varying time domain channel accordingto:{circumflex over (R)}=G _(rx) *ĤG _(tx)

where the matrix {circumflex over (R)}, is an estimate of the matrix Rdefined as herein, which is calculated as above, using the matrix Ĥwhich comprises an estimate of the time varying time domain channelmatrix H defined as herein;

determining a MMSE sequence from an observation according to:{circumflex over (R)}=({circumflex over (R)}*{circumflex over (R)}+σ ²I)⁻¹ {circumflex over (R)}*Y

where {circumflex over (R)} is defined as above, {circumflex over (X)}is an estimate of X defined as herein, and σ², I and Y are defined asherein.

In one arrangement the elements {circumflex over (R)}_(j)*[i] arereplaced by an MMSE weighting factor matrix.

Further scope of applicability of the present invention will becomeapparent from the detailed description given hereinafter. However, itshould be understood that the detailed description and specificexamples, while indicating preferred embodiments of the invention, aregiven by way of illustration only, since various changes andmodifications within the spirit and scope of the invention will becomeapparent to those skilled in the art from this detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

Further disclosure, improvements, advantages, features and aspects ofthe present invention may be better understood by those skilled in therelevant art by reference to the following description of preferredembodiments taken in conjunction with the accompanying drawings, whichare given by way of illustration only, and thus are not limiting to thescope of the present invention, and in which:

FIG. 1 graphically illustrates a normalised power delay profile for anindoor wireless channel;

FIG. 2 illustrates the use of a cyclic prefix for a packet in an OFDMwireless communications network;

FIG. 3( a) graphically illustrates an actual and estimated frequencydomain channel response for an indoor wireless channel;

FIG. 3( b) graphically illustrates an equalised observation for anindoor channel;

FIG. 4 graphically illustrates a normalised power delay profile for anoutdoor wireless channel;

FIG. 5( a) graphically illustrates an actual and estimated frequencydomain channel response for an outdoor wireless channel;

FIG. 5( b) graphically illustrates an equalised received observation foran outdoor wireless channel showing heavy distortion by ISI and ICIeffects;

FIG. 6 is a block functional diagram of an exemplary transmitter usedfor transmitting signals in a wireless multicarrier communicationsystem;

FIG. 7 is a graphical illustration of average power distribution ofdiagonal components of a matrix representation of a channel transferfunction representing Inter-Carrier Interference (ICI) and Inter-SymbolInterference (ISI), respectively;

FIG. 8 is a block functional diagram representing a receiver system inaccordance with a preferred embodiment of the present invention;

FIG. 9 is a block functional diagram representing a channel estimatorcomponent of the receiver of FIG. 8;

FIG. 10 illustrates a construction of a matrix A which may be used tobuild an MMSE filter that is employed during a channel estimationboot-up in the channel estimator of FIG. 9;

FIG. 11 illustrates the result of a tap selection near the start of testwireless transmission;

FIG. 12 illustrates the result of a tap selection near the end of a testwireless transmission;

FIG. 13 illustrates windows of an observation and training sequencesemployed in a sliding-window time-domain channel estimator in thechannel estimator of FIG. 9;

FIG. 14 is a block functional diagram representing a generic equaliserinterface model for an equaliser component of the receiver of FIG. 8;

FIG. 15 is a block functional diagram illustrating a 2-tap Maximum RatioCombiner, 2-tap Interference Cancellation model incorporated in thegeneric equaliser interface model of FIG. 14;

FIG. 16 is a block functional diagram of the decoder block of FIG. 8having an FEC decoder that outputs a full codeword sequence estimate;and

FIG. 17 is a block functional diagram of the decoder block of FIG. 8having an FEC decoder that outputs an information sequence estimate

DETAILED DESCRIPTION

The following terms used in the description are given the followingmeanings:

SNR: Signal to Noise Ratio;

MMSE: Minimum Mean Square Error;

ZF: Zero Forcing

AWGN: Additive White Gaussian Noise

ICI: Inter Carrier Interference;

ISI: Inter Symbol Interference;

RMS delay spread: Root Mean Squared Delay Spread

FEC: Forward Error Correction

IC: Interference Cancellation

FFT: Fast Fourier Transform

IFFT: Inverse FFT

Multicarrier Communication Model

The model presented in this section is employed throughout thedescription. It may be applied to any multicarrier communication systemwhich simultaneously transmits data in multiple orthogonal signallingdimensions, e.g. an OFDM system. In the OFDM case described herein,processing takes place in two domains, the time domain and the frequencydomain, with conversions between domains as required.

Consider a multicarrier channel which is subject to ISI and/or ICI. Sucha channel may be modelled as described in eqn 1.Y=G* _(rx) HG _(tx) X+n  1

where

H is a time-domain linear-convolution matrix used to model the linearconvolution of the transmitted signal with the multipath radio channel;

X is a vector representing L transmitted frequency domain symbols;

Y is a vector representing the L multicarrier symbols observed at thereceiver, in the frequency domain;

n is additive white Gaussian noise;

G_(tx) is the transmit-side transform matrix, which translatestransmitted multicarrier symbols from the frequency domain into the timedomain; and

G*_(rx) is the receive-side transform matrix which translates receivedtime-domain multicarrier symbols into the frequency domain. Here, andthroughout this document, the operation A* represents the conjugatetranspose of the matrix (or vector) A.

It is assumed that each multicarrier symbol in X is generated using atransmitter that uses symbol mapping in the frequency domain. Thetransmitter may have, for example, the format shown in transmitter 100of FIG. 6. Here a set of input information bits 102 is encoded using aFEC encoder 104, and the encoded bits are interleaved by interleaver106, prior to being mapped by symbol mapper 108 onto symbols in thefrequency domain. A domain converter 110 then translates the symbols toprovide an output 112 for transmission in the time domain (e.g. thedomain converter 112 may be an IFFT in OFDM). The receiver arrangementsdescribed below are not tied to specific transmitters, provided thatreceiver side components are consistent with coding constraints used atthe transmitter side. For example, if a FEC code is employed in encoder104 then the decoder used at the receiver will assume the same codeconstraints as those assumed by the encoder 104 at the transmitter 100.

The extended transform matrices, G_(tx) and have dimensions which scalewith the number of multicarrier symbols, L, e.g. for a length L packet.These matrices are constructed from the transform matrices F_(tx) andF_(rx)* respectively, via a Kronecker product with the L×L identitymatrix. The individual elements of the matrices which are used totranslate signals between domains are determined by the type ofmulticarrier system being employed. For example, when modelling aconventional N-subcarrier OFDM system, translation occurs between thetime domain and frequency domain, and each transmitted time domainsymbol is cyclic prefixed with some number of samples, C, of itself. Inthis case the model employs

$F_{tx} = \begin{bmatrix}W_{C} \\W_{N}\end{bmatrix}$ and ${F_{rx} = \begin{bmatrix}Z \\W_{N}\end{bmatrix}},$

where W_(N) represents the N×N Fourier matrix, having elements

${w_{i,k} = {{\mathbb{e}}^{\frac{2\;\pi\sqrt{- 1}{({i - 1})}{({k - 1 - {N/2}})}}{N}}/\sqrt{N}}},$where i and k represent row and column number respectively, both beingindexed starting from 1. W_(C) contains the last C rows of W_(N). Theguard samples may be discarded at the receiver, and this is modelled bythe Z component of F_(rx), which has the same dimensions as W_(C) andcontains zero-valued elements. Any OFDM-symbol timing-error that existsbetween the transmitter and receiver is assumed to be absorbed into thetime-domain radio-channel model. Another example model occurs when thecyclic prefix is replaced by an empty guard at the transmitter. In thiscase both F_(tx) and F_(rx) are equivalent to the F_(rx) matrix shownfor the OFDM example above. Another example occurs when no cyclic prefixor guard interval is used, in which case both F_(tx) and F_(rx) areequivalent to the Fourier matrix W_(N).

In order to provide insight into the effects of inter-symbol andinter-carrier interference, the channel transfer function may berepresented in the absence of noise by the matrix R, as defined in Eqn2.R=G* _(rx) HG _(tX)  2

Consider the model for a conventional OFDM system, with a static timedomain channel having length that is constrained to within one prefixedsymbol period. The matrix R may be constructed as shown in Eqn 3, for anexample case of L=4.

$\begin{matrix}{R = \begin{bmatrix}R_{0} & \; & \; & \; \\R_{1} & R_{0} & \; & \; \\\; & R_{1} & R_{0} & \; \\\; & \; & R_{1} & R_{0}\end{bmatrix}} & 3\end{matrix}$

The component matrix R₀ is constructed according to Eqn 4, where H₀ istaken from the first N+C rows and the first N+C columns of H.R ₀ =F* _(rx) H ₀ F _(tx)  4

Similarly R₁ is constructed according to Eqn 5, where H₁ is taken fromrows N+C+1 through 2(N+C), and the first N+C columns of H.R ₁ =F ^(*) _(rx) H ₁ F _(tx)  5

Using the above definitions, each received symbol may be expressed asshown in Eqn 6, where bracketed terms indicate the symbol period index.Y[i]=R ₀ X[i]+R ₁ X[i−1]+n[i]  6

The model may be extended to the case of a time-varying channel, byallowing of the content of R₀ and R₁ to change with time, as shown inEqn 7, in which the terms are indexed with respect to the symbol periodindex of the received OFDM symbol observation.Y[i]=R ₀ [i]X[i]+R ₁ [i]X[i−1]+n[i]  7

Additionally in the case where the delay spread extends beyond N+C wemay generalise the interference model to

${Y\lbrack i\rbrack} = {{\sum\limits_{j}^{\;}\;{{R_{j}\lbrack i\rbrack}{X\left\lbrack {i - j} \right\rbrack}}} + {n\lbrack i\rbrack}}$

The model may be also be adapted to include special OFDM symbolconstructions, e.g. in the case of the long preamble dictated by theIEEE 802.11a standard. This standard specifies that data symbols are tobe constructed in the manner described above, with each 64 sample timedomain OFDM symbol being prefixed with a copy of its final 16 samples.In contrast, the long preamble may be considered as two replicatedback-to-back OFDM symbols, each of 64 samples length, with a cyclicprefix of length 32 samples prefixing them. The long preamble may beincorporated into the model, over two OFDM symbol periods, using thefollowing special transform matrices.

$F_{txLP} = \begin{bmatrix}W_{C\; 32} & \; \\\; & Z_{96 \times 64} \\W_{64} & \; \\Z_{64 \times 64} & W_{64}\end{bmatrix}$ and ${F_{rxLP} = \begin{bmatrix}Z_{32 \times 64} & \; \\\; & Z_{96 \times 64} \\W_{64} & \; \\Z_{64 \times 64} & W_{64}\end{bmatrix}},$

where W₆₄ represents the 64×64 Fourier matrix and W_(C32) contains thelast 32 rows of W₆₄. Component matrices which contain zero-valuedelements are denoted Z_(d), where d specifies their dimensions. Thedouble symbol length contribution to R is then constructed as follows,where H_(LP) is taken from the first 160 rows and the first 160 columnsof H.R _(LP) =F* _(rxLP) H _(LP) F _(txLP)

FIG. 6 shows the average power of the diagonals of R₀ and R₁, indexedfrom the main diagonal at zero, for an example model representing anIEEE 802.11a OFDM system being employed in an outdoor environment. Graph120 shows the average power of R₀, and graph 122 shows the average powerof R₁. The IEEE 802.20 Typical Urban (Case-IV) channel model [4] wasused to generate H for the purpose of the example.

The main diagonal of R₀, denoted h₀, represents the direct channelcontribution, and the non-principal diagonals of R₀ model inter-carrierinterference (ICI) from other subcarriers within the same receivedsymbol.

The components of R₁ model inter-symbol interference (ISI) from theprevious symbol, the most significant being the main diagonal, denotedh₁.

The length of the IEEE 802.20 (Case-IV) delay spread is 3.2 μs, which iswell in excess of the 0.8 μs of cyclic prefix protection offered by theIEEE 802.11a symbols. As a result, significant levels of both ISI andICI are observed in graphs 120 and 122. However, both R₀ and R₁ arediagonally dominant, and the model provides some insight into whichchannel components present the most significant contribution to thedistortion of the received symbol. The model suggests the following:

-   -   A significant amount of the transmitted symbol energy may be        recovered for the symbol, by correctly combining received        observations for the symbol and the (i+1)^(th) symbol, using h₀        and h₁ respectively.    -   A significant amount of interference may be removed from the itn        symbol after the combining of received observations, by        correctly cancelling contributions from the i^(th) symbol, the        (i−1)^(th) symbol and the (i−1)^(th) symbol, using h₀ and h₁        respectively.    -   Any non-principal diagonal components of R₀ and R₁ may also be        employed for combining and cancellation, however diminishing        returns are to be expected when moving outwards from the        principal diagonals of R₀ and R₁.    -   The symmetry about the main diagonals of R₀ and R₁, and the        equality of the average off-diagonal power between R₀ and R₁ at        the same offset index, implies that the components present        themselves as equal candidates for combining or cancellation in        groups of four.

In the description hereinafter, embodiments of the present invention arepresented in the context of an OFDM system, and hence thetransformations are undertaken between the time domain, and thefrequency domain. These embodiments of the present invention may beequally applicable to other transformations. Furthermore, embodiments ofthe present invention are applicable to both single antenna andmulti-antenna systems. The results presented in the discussion thatfollows focus upon the single antenna case, however detail is providedregarding extensions of the described embodiments of the presentinvention to multi-antenna cases. The applicability of embodiments ofthe invention to single antenna scenarios makes them especially valuablein cases where multiple-antenna systems cannot be employed, e.g. someportable devices. The inventor also notes that the describedarrangements may find usefulness in many digital communication systems,including the following:

IEEE 802.11, 802.16 & 802.20;

OFDM and OFDMA;

WiBro (Wireless Broadband);

UWB 802.15, LTE 3G;

MC-CDMA;

DVB (Digital Video Broadcasting),

DSL and other wired multicarrier standards e.g. HomePlug.

System Level Description

Advanced receiver techniques for long-delay spread OFDM communicationsystems are proposed, using the receiver structure 200 shown in FIG. 8.The functional blocks 204-214 of FIG. 8 may be implemented as softwarerunning on a receiver device that may include one or moremicroprocessors. Alternatively, the functional blocks 204-214 may beimplemented as customised hardware provided in a receiver device, forexample using one or more Digital Signal Processors (DSPs), FieldProgrammable Gate Arrays (FPGAs) or Application Specific IntegratedCircuits (ASICs).

It is assumed that data is transmitted in packets of OFDM symbols, andthat each packet contains a preamble that is known to the receiver 200.The received observation input 202 is provided to a channel estimator204 and to domain converter 208. As described below with reference toFIG. 9, the channel estimator 204 performs channel estimation boot-up inthe time domain. The channel estimate is then tracked, during packetreception, by the channel estimator block 204. The channel estimate isprovided to domain converter 206, which transforms the time-domainestimate into a frequency-domain channel estimate, as described in moredetail below. The channel estimator 204 may also generate channelstatistics that are provided to equaliser 210.

The domain converter 208 transforms the input 202 from the time domainto the frequency domain and provides the transformed observation to theequaliser 210. Domain converter 208 may use a Fast Fourier Transform(FFT).

The equalizer block 210 uses the received observation (transformed bydomain converter 208), the channel estimate (transformed by domainconverter 206), and an estimate of transmitted symbols (fed back frommemory 216 and decoder 212), to remove distortion due to channeleffects. The resulting output of equaliser 210 is passed to a decoder212, which provides an updated estimate 218 of the transmitted data. Theupdated estimate may be stored in a storage device such as memory 216.The equaliser 210 is described in more detail with reference to FIGS. 14and 15. The equalizer 210 and decoder 212 together provide a symbolestimator that outputs an estimate of the transmitted data.

The process may then be iterated. Decoder outcomes 218 are employed insubsequent execution of the channel estimation 204 and equalizationblocks 210. The estimated output 218 is in the frequency domain and sodomain converter 214 may be used to convert the estimated output 218into the time domain for use by the channel estimator 204. Domainconverter 214 may use an Inverse Fast Fourier Transform (IFFT). Theupdate rate of the iteration may, for example, be per sample, per OFDMsymbol or per FEC block. The iterative process may run until a maximumnumber of iterations has been reached, or iteration may be stoppedearly. In one embodiment, the stopping criteria may be controlled by anFEC decoder that is a functional subunit of decoder 212. For example,the FEC may report a valid decoding, or it may report a soft belief thatthe decoding is valid, and that belief may be above some thresholdvalue. In another embodiment the stopping criteria may be controlledusing a checksum, e.g. a cyclic redundancy check, which is performedacross the FEC decoded outputs. Examples of the decoder 212 aredescribed below with reference to FIGS. 16 and 17.

The domain converter blocks 214, 206 and 208 are used to translatesignals between the time domain and the frequency domain. Domainconverters 208, 214 user may use FFT and IFFT techniques respectively.The techniques used in domain converter 206 are described in more detailbelow.

In another embodiment, the receiver system has multiple receiveantennas, and an individual channel estimator block 204 then exists foreach receive antenna.

In another embodiment, the system has multiple receive antennas and somenumber of channel estimator blocks exist in the receiver, is the numberof channel estimator blocks being less than the number of antennas. Inthis case the available channel estimator block(s) may be used toprocess incoming signals from a plurality of antennas, on atime-multiplexed basis.

In embodiments that have multiple receive antennas, multiple channeldomain converter blocks 206 may also exist. In the case when the numberof domain converter blocks 206 equals the number of channel estimatorblocks 204, then each channel estimator block is connected to a separatedomain converter block. In the case when the number of channel estimatorblocks exceeds the number of domain converter blocks, the domainconverter blocks are used to convert channel converter block outputs ona time-multiplexed basis. All domain converter block outputs arepresented as input to the equalizer block 210, where they are combinedas described in the equalizer block section below.

Component Level Description

Channel Estimator

The channel estimation block 204 is expanded in FIG. 9 and eachfunctional block is described in the text that follows. The channelestimation block has a switch 302 that determines whether theobservation input 202 is provided to a preamble boot-up block 304 or atracking block 314.

Boot-Up Processing

With reference to FIGS. 8 and 9, an initial estimate is generated via apreamble boot-up process performed by boot-up block 304, which operateswhen switch S 302 is in the P position. This process uses the receivedobservation 202, and knowledge of the transmitted preamble to produce anestimate of complex time-domain channel taps. Examples of methods forproviding the initial estimate are described below. It is common for asection of samples at the beginning of the received observation to bedistorted, due to automatic-gain-control (AGC) events, and in such acase that section should be discarded.

Linear Filter

An initial estimate of the time domain channel, having length m, may begenerated using Eqn 8.ĥ _(t)=(A*A+σ ² I)⁻¹ A*y  8

where

A is a cropped linear convolution matrix, of width m columns,representing the known transmitted preamble in the time domain. Thematrix A is obtained by selecting a window of rows 128 from theconvolution matrix 124, as shown in FIG. 10. Rows 126 and 130 arediscarded from the convolution matrix 124. The window size and positionused for selecting A may be selected offline, e.g. according to thefollowing criteria:

-   -   Minimization of the mean-squared error of the channel estimate;    -   Avoidance of early preamble samples due to potential AGC        distortion;    -   Reduction of implementation complexity.

I is the m×m identity matrix

y is a vector of time domain samples from the received observation, timealigned to the first column of samples in A.

σ² is the minimum mean-squared error (MMSE) noise scaling factor, and isnormally set to the power of the noise perturbing the observation y.Selecting σ²=0 generates a Zero Forcing (ZF) filter.

The optimal setting for σ² may be calculated using the received signalnoise power, by recalculating of the filter matrix (A*A+σ²I)⁻¹A* atdifferent SNR operating points. Alternatively, by assuming a fixed valuefor σ², the filter matrix may be precomputed and stored. Moreover,several matrix instances may be stored in a lookup table, for differentvalues of σ², and used for different regions of SNR operating points.

In some embodiments the preamble sequence may contain a section ofrepeated OFDM symbols, which have a set of zero-valued subcarriers inthe frequency domain. Such a repetition of OFDM symbols can result inthe final symbol in the series being well guarded against ISI and ICIdistortion, due to the symbols that precede it acting as a long cyclicprefix. An estimate of the received noise power may then be calculatedin the frequency domain, from the variance of the received values atsubcarriers which correspond to the zero-valued subcarriers at thetransmitter. Such an example occurs in the case of the 802.11a waveform,where the last 64 time domain samples of the short preamble areprotected by an effective cyclic prefix of length 64 samples. Forexample, at a bandwidth of 20 MHz this corresponds to a protection of3.2 μs.

In some embodiments the transmit waveform may include a sequence knownto the receiver 200, at a point in the packet that is not necessarilyaligned with the start of the packet. An example of such a transmissionis the midamble sequence dictated in the 802.16 standard. An estimate ofthe channel can be obtained during packet reception in such cases, byemploying the boot-up processing 304.

Sliding Correlator

The boot-up unit 304 may calculate an initial channel estimate using asliding correlator (matched filter), by correlating the receivedobservation 202 in the time domain, with a section of stored time-domainpreamble samples.

Using the definitions of the previous section regarding the LinearFilter we may state the Sliding Correlator output asĥ _(t) =A*y  9

Techniques known for implementing linear convolution (as shown above)using cyclic convolution techniques (based on FFTs) may be employed hereto reduce complexity.

In calculating the channel estimate using equation 9, an accumulator maybe used for each of the bins used. The values stored in the accumulatorsmay be subsequently used by the tracking module 314.

In cases where the time-domain preamble is periodic, e.g. as dictated bythe IEEE 802.11a wireless standard, the sliding correlator may producefalse taps when long-delay-spread channels are being estimated. Thesefalse taps occur due to an unfavourable autocorrelation sequence of thetime-domain preamble. In such cases the linear MMSE filter approachdescribed above can provide a more accurate channel estimate than thesliding correlator, by simultaneously suppressing noise andautocorrelation spikes.

Integrated MMSE and Sliding Correlator

As can be seen by comparing equations 8 and 9 the sliding correlatorforms part of the Linear Filter (MMSE) front end. The MMSE output may becalculated from the Sliding Correlator output by multiplication of thechannel estimate vector by the decorrelating matrix (A*A+σ²I)⁻¹. Thismatrix could be computed from time to time. An alternative approach isto use the average matrix E_(A)[(A*A+σ²I)⁻¹] which we approximate as(E_(A) [A*A]+σ²I)⁻¹. The matrix E_(A) [A*A] has structure that allowsapproximate implementation via linear convolution. The filter employedis that corresponding to the centre rows of E_(A)[A*A]. In typical OFDMwaveform implementations the output of the IFFT may be non-white and thefilter described above accounts for this correlation, reducing theeffects of using non-white training sequences for channel estimation.

Channel Statistics Calculator

The channel statistics calculator block 308 receives the output of thepreamble boot-up block 304 and the tracking block 314 and provides anestimate of the average power, and RMS delay spread of the channel,using the length m time domain channel estimate, ĥ_(t). The output 318of the statistics calculator block 308 is provided to combiner 310 andtap selector 306 and also to equaliser 210.

The delay spread result is given with respect to the OFDM symbol period.The average power is calculated according to Eqn 10. The RMS delayspread is calculated according to Eqn 11, where ĥ_(ti) represents thei^(th) tap of the channel estimate.

$\begin{matrix}{P_{av} = {\sum\limits_{i = 0}^{m - 1}\;{{{\hat{h}}_{ti}}^{2}/m}}} & 10 \\{{{RMS}\mspace{14mu}{Delay}\mspace{14mu}{Spread}} = \sqrt{\frac{\sum\limits_{i = 0}^{m - 1}\;{i{{\hat{h}}_{ti}}^{2}}}{\sum\limits_{i = 0}^{m - 1}{{\hat{h}}_{ti}}^{2}}}} & 11\end{matrix}$

Results from the statistics calculator 308 may be used by controlmechanisms, such as the tap selection process described below, or fordiagnostics. In some implementations of the receiver 200 the calculatorblock 308 may be omitted to reduce overall implementation complexity.

Tap Selection

The tap selector block 306 receives outputs from the preamble boot-upblock 304 and statistics calculator 308 and compares the power of eachestimated time-domain tap with a threshold value. The tap selector 306discards those taps with power below the threshold. The threshold may besome fixed value, or may be some function of the estimate, which is setdynamically as the estimate is updated, e.g. via a lookup table. Forexample, the threshold power may be set at some percentage of themaximum tap power. Alternatively, the threshold may be set according tosome function of the average or maximum power of the channel estimate,e.g. some scaling of the square root of the average power.

For each tap which passes the threshold test, the tap selector 306 mayalso output a group of taps either side. The size of the groups may befixed or may be calculated dynamically, e.g. using some function of theRMS delay spread statistic provided by the statistics calculator 308.

In other embodiments, some maximum number of taps which meet thecriteria may be selected. For example, where the number of taps selectedis a predetermined fixed value, or where the number of selected taps isbased upon a predetermined statistic of the channel estimate, such as,for example, some function of the RMS delay spread statistic.

In another embodiment, the tap selection process may be locked at somepoint in time. The lock time may be predetermined, or may be chosendynamically, e.g. the lock time may be obtained from a lookup tableduring packet reception, according to one or more receiver statemetrics, such as, for example, an estimate of noise power in thereceived observation

In other embodiments, a tap selector block may be placed after thechannel estimate combiner block 310 in addition to, or instead of, thetap selector 306 placed after the preamble boot-up block 304, and/orplaced after the channel tracking block 314.

Tap selection may provide the advantage of reduced implementationcomplexity for processes which make use of the channel estimate. Tapselection can also provide a smoothing effect in the frequency domainrepresentation of the channel.

With reference to FIG. 11 and FIG. 12 we show the result of tapselection near the start and end of an IEEE 802.11 test transmissionoperating at 5 GHz. FIG. 11 shows the time-domain radio-channel power ofsymbol 10 and FIG. 12 shows the time-domain radio-channel power ofsymbol 26. This example assumes an IEEE 802.20 Case IV channel model,with relative receiver mobility of 230 kmph. We see that the actualradio channel (indicated by circles) changes significantly between thetwo OFDM symbols (which are separated by 15 OFDM symbols, i.e. 60 μs).We also note that the tap selector has selected different taps(indicated by dots) for admission to the Time-Frequency domain converter206. For example, the 63rd tap has fallen below the threshold value insymbol 26.

Channel Estimate Tracker

Upon completion of preamble processing, the channel estimator 204 shiftsswitch S 302 to the T position and the channel estimate tracking processof block 314 is enabled. The tracking process of block 314 and combiningprocess of block 310 (described below) allow the receiver 200 to operatein the presence of a time-varying channel, such as that encounteredduring relative mobility between the transmitter and receiver, and/or tobe robust to the presence of time-varying frequency offset and other RFeffects. The tracker 314 makes use of the received observation 202 and atime-domain estimate 316 of the transmitted data for a particular OFDMsymbol. The estimate 316 is fed back from receiver components 214, 216,212 external to the channel estimator 204. The output of the tracker 314is provided to the statistics calculator 308 and the combiner 310 andmay also be provided to a tap selector.

From time to time a set of up to m accumulators, indexed by delay bin inthe time-domain channel estimate, is updated with the next segment oftime domain observation and time-domain training. The time-domaintraining could be supplied from many sources such as FEC decoderoutcomes, pilot, preambles or hard decisions on the symbols. Thus, thetime-domain training may be symbol estimates X fed back to the tracker314 from the decoder 212 in the iterative structure of FIG. 8.

The new training symbols are pushed onto the head of a buffer containingall previous training symbols as illustrated in FIG. 13, which showsthree bins 140, 142, 144 corresponding to delays of zero, 1 and nsymbols respectively. The correlation update for each delay bin 140,142, 144 is then calculated by correlating the observation 150 with awindow of the training buffer of the same length as the observation 150(the window length is shown as shaded elements in FIG. 13). The windowof the training buffer is offset from the end of the buffer by an amountequal to the delay bin under test and is accumulated into the partialcorrelation result. For example, the offset of bin 140 is zero symbols,the offset 152 of bin 142 is one symbol and the offset 154 of bin 144 isn symbols. The accumulation may be executed using an auto-regressivefilter, such as that described in relation to the Channel EstimateCombiner 310, which follows.

The buffer may be used in a hand-over from the boot-up estimator 304 tothe tracker 314. In the hand-over the training changes from the knownpreamble to estimated transmitted symbols, delivered for example by thedecoder 212.

A selected subset of taps may be tracked, as dictated by the tapselection block 306, thus reducing implementation complexity.

The channel estimate output by the tracker 314 may be calculated usingthe accumulators, resulting in sliding window correlation outputs, andmay also be further processed in a manner similar to that describedabove for the boot-up process of block 304, resulting in LMMSE or ZFoutputs.

Sliding window correlation may be implemented using an accumulator and ashift register of length equal to the window length or using anauto-regression.

The LMMSE filter described in the boot-up process is of particularinterest in tracking mode. The sliding correlator is low complexity butmay have training sequence autocorrelation artefacts that degradechannel estimation quality. The subsequent application of thedecorrelating filter, either in matrix or linear convolution form maycorrect this shortcoming.

The transition from Boot-Up to Tracking mode may be achieved by assumingthat the boot-up processor 304 had used the raw sliding correlator A*.

As mentioned above, the tracker 314 may use values stored inaccumulators by the boot-up module 304 during the initial estimation ofthe channel parameters.

In another embodiment, techniques known for estimating the channel inthe frequency domain may be employed by tracker 314 to track thechannel. For example, the channel estimate in the frequency domain maybe obtained by division or conjugate multiplication of the receivedfrequency domain observation (e.g. the output of converter 208), by anestimate of the transmitted sequence in the frequency domain. Theestimate of the transmitted sequence may be provided by the FEC decoder504, 510, as described in the Decoder block description with referenceto FIGS. 16 and 17. Some frequency domain estimation techniques aredescribed, for example, in published application US2004/0264561 in thename of Alexander et al, filed 23 Jul. 2004.

In another embodiment the channel-estimate tracking method used bytracker 314 may change from one technique to another at some pointduring reception of a packet. For example, the method used by thetracker 314 may change from a time-domain estimation to afrequency-domain estimation at some point in time during packetreception, using any of the channel estimation methods described above.In one embodiment, the point in time at which the method is changed maybe a fixed delay with respect to the start of packet reception. Inanother embodiment, the point in time is determined dynamically by somesystem state metric. For example, the system state metric may be astatistic obtained from the time domain channel estimate, e.g. somefunction of the delay spread calculated by statistics calculator 308.

Channel Estimate Combiner

The channel estimate combiner 310 has as inputs the outputs of tapselector 306, tracker 314, and statistics calculator 308.

The channel estimate combiner 310 provides an output that filters acurrent estimate in light of previous estimates. In one arrangement thecombiner 310 may use memory to provide an auto-regressed update of thechannel estimate, ĥ_(t), with the new channel estimate from the channelestimate tracker block 314, ĥ_(t(new)), according to Eqn 12.ĥ _(t) =αĥ _(t(new))+(1−α)ĥ _(t)  12

The auto-regression factor, α, is given a value between zero and one.This value may be fixed or may change over time. For example, a may beupdated over time via a lookup table, or updated adaptively according tosome system state metrics. Moreover, a higher order filter may be used,to combine several states of the channel estimate, with an independentauto-regression factor assigned to each state.

In another embodiment, individual taps in the time-domain channelestimate, or groups of taps, may be assigned different weightingfactors, and may be updated as described for the cases above.

The output of the channel estimate combiner 310 is a time-domain channelestimate 312, which is output to the domain converter 206.

Domain Converters

The domain converters 206, 208, 214 are used to translate signalsbetween the time domain and frequency domain. Embodiments of the presentinvention described here are presented in the context of an OFDM system.Other embodiments may also be derived for different models that useconversions between different domains.

Time Domain—Frequency Domain (Rx Observation) 208

The received observations 202 are converted, on a per-OFDM symbol basis,into the frequency domain using the standard FFT method, discarding thecyclic prefixed guard portion of the symbol.

Time Domain—Frequency Domain (Channel) 206

In order to drive the equalization block 210, frequency domain versionsof both h₀ and h₁ may be required. Furthermore, off-diagonal componentsof R₀ and R₁ may also be employed. In addition, higher order models maybe used, i.e. relying on R₂, R₃. etc.

The full complexity method for converting the channel estimate into thefrequency domain, involves calculating the matrix R₀ according to Eqn 4.For a conventional OFDM system with N subcarriers and C cyclic prefixsamples, H₀ may be generated from the first N+C elements of thetime-domain channel estimate ĥ_(t). Calculation of R₀ via this methodhas O(N³) complexity. For example, in the case of the 802.11a standard,approximately 6×10⁵ multiply operations are required in the calculation.In the case of the 256 subcarrier OFDM variant of the 802.16 standard,when a guard of ¼ is selected, more than 3×10⁷ multiply operations arerequired. The matrix R₁ may be generated in a similar manner. H₁ may begenerated from rows N+C+1 through 2(N+C) and the first N+C columns ofthe time-domain channel estimate.

The model presented in the early part of this description highlights thesignificance of the h₀ and h₁ vectors, for effective signal combiningand cancellation. Moreover, it indicates that the significance of thenon-principal diagonal components of R₀ and R₁ diminishes quickly whenmoving away from the principal diagonal. This motivates the developmentof a computationally efficient method for calculating h₀ and h₁. We nowdescribe two computationally efficient methods for this calculation.Either method may be used for domain converter 206. Method 1 is based ona circulant extension of the time-domain model. Method 2 is based on alinear diagonal decomposition of the time domain model.

Method  1 (Circulant  Extension):The  vectors  h₀  and  h₁  are  specified  via $\begin{matrix}{h_{0} = {{diag}\left( {F_{rx}^{*}H_{0}F_{tx}} \right)}} \\{= {{diag}\left( {W_{N}{LF}_{tx}} \right)}}\end{matrix}$ $\begin{matrix}{h_{1} = {{diag}\left( {F_{rx}^{*}H_{1}F_{tx}} \right)}} \\{= {{diag}\left( {W_{N}{UW}_{N}^{*}} \right)}}\end{matrix}$

Where the matrix L is the N×N+C sub-matrix of H₀ starting at row C+1 andcolumn 1. The matrix U is the N×N sub-matrix of H₁ starting at row C+1and column 1 (assuming that row and column indexes start from 1).

Since both L and U are sub-matrices of a convolution matrix, they canalways be extended to form larger, 2N×2N, circulant matrices, i.e.matrices where each row is obtained from the previous row via cyclicshift one place to the right. Denote these constructed circulantmatrices as L₂ and U₂. Note that these matrices are only used in thederivation of the method, and are not required for implementation of themethod.

Circulant matrices are completely specified by their first row. Usingthe well known MATLAB™ notation, the first row of L₂ is the vectorl=[L(1,1:N+C),L(N,1:N−C)]. The first row of U₂ is the vector u=[U(1,1:N),0_(1×N)]. Other cyclic rotations of l are also valid candidates forthe first row of L₂. Similarly, other cyclic rotations of u are alsovalid candidates for the first row of U₂.

The eigenvectors of a circulant matrix are the columns of the Fouriermatrix and the eigenvalues are the Fourier transform of the first row ofthe circulant matrix. Let l_(f) be the Fourier transform of l and letu_(f) be the Fourier transform of u. These transforms may be computedefficiently using 2N point fast Fourier transforms. ThusL ₂ =W _(2N)diag(l _(f))W _(2N)*U ₂ =W _(2N)diag(u _(f))W _(2N)*

And furthermore, L is the N×N+C top-left sub-matrix of L₂, and U is theN×N top-left sub-matrix of U₂. LetJ ₁=(I _(N)0_(N))J ₂=(I _(N+C)0_(N+C×2N−N−C))

The vectors h₀ and h₁ may now be re-written as followsh ₀ =diag(W _(N) J ₁ W _(2N)diag(l _(f))W _(2N) *J ₂ *F _(tx))h ₁ =diag(W _(N) J ₁ W _(2N)diag(u _(f))W _(2N) *J ₁ *W _(N)*)DefiningZ ₁ =W _(N) J ₁ W _(2N)Z ₂ =W _(2N) *J ₂ F _(tx)And re-arranging leads toh ₀=(Z ₁ ⊙Z ₂)l _(f)h ₁=(Z ₁ ⊙Z ₁*)u _(f)

Here, and elsewhere in this document, ⊙ denotes element-wise product(sometimes called Hadamard product). Note that the matrices Z₁⊙Z₂ and Z₁⊙Z₁* are constant and do not depend on the received signal or thechannel. They can be pre-computed and stored in memory.

Using this method requires only two 2N point fast Fourier transforms andtwo matrix-vector multiplications. Note that matrix vectormultiplications can be performed in parallel, i.e. per sub-carrier. Thetotal number of operations required is approximately 4N log 2N+4N².

For 802.11a this is 1.8×10⁴ multiply operations, and for 802.16 with 256sub-carriers it is approximately 2.7×10⁵ multiply operations. Thisrepresents less than 1% of number of operations required by the fullcomplexity method respectively for each case.

The number of operations can be further reduced by making use of thespecial structure of Z₁⊙Z₂* and Z₁ ⊙Z₁*, which are sparse. This furtherreduces the number of multiplications required.

The number of computations per symbol may also be reduced by computingthe coefficients for a reduced number of sub-carriers. In fact it may beadvantageous to alternate between computations for different sets ofsub-carriers. This could be useful when the channel coherence frequencyis large enough to enable re-use of channel coefficients across severalsub-carriers, without having to actually compute each one individually.

In the case that the time domain channel estimate used to construct thematrices L and U has only a small number of taps, it may be moreefficient to directly compute the discrete Fourier transform viamultiplication of complex sinusoids by the time domain tap weights. Forexample, for 802.11a, this method can be more efficient with less than 7non-zero time domain taps.

Method  2 (Linear  Diagonal  Decomposition):The  vectors  h₀  and  h₁  are  specified  via $\begin{matrix}{h_{0} = {{diag}\left( {F_{rx}^{*}H_{0}F_{tx}} \right)}} \\{= {{diag}\left( {F_{rx}^{*}{\sum\limits_{i = 0}^{N + C - 1}\;{{\hat{h}}_{ti}D_{- i}F_{tx}}}} \right)}} \\{= {\sum\limits_{i = 0}^{N + C - 1}{{\hat{h}}_{ti}{{diag}\left( {F_{rx}^{*}D_{- i}F_{tx}} \right)}}}}\end{matrix}$ $\begin{matrix}{h_{1} = {{diag}\left( {F_{rx}^{*}H_{1}F_{tx}} \right)}} \\{= {{diag}\left( {F_{rx}^{*}{\sum\limits_{i = 0}^{N + C - 1}\;{{\hat{h}}_{ti}D_{N + C - 1 - i}F_{tx}}}} \right)}} \\{= {\sum\limits_{i = 0}^{N + C - 1}{{\hat{h}}_{ti}{{diag}\left( {F_{rx}^{*}D_{N + C - 1 - i}F_{tx}} \right)}}}}\end{matrix}$

Where D_(i) is a matrix that is zero in every element except alongdiagonal i, and one in every element along diagonal i, where byconvention i=0 denotes the principal diagonal and negative values of iindicate diagonals below the principal diagonal. Define the matrix M₀whose column number i is the vector formed fromdiag(F*_(rx)D_(−i)F_(tx)). Similarly define the matrix M₁ whose columnnumber i is the vector formed from diag(F*_(rx)D_(N+C−1−i)F_(tx)). Thenthe conversion can be implemented according toh ₀ =M ₀ ĥ _(t)h ₁ =M ₁ ĥ _(t)This requires only two complex matrix-vector multiplications, which isapproximately 2N(N+C) operations. The matrices M₀ and M₁ do not dependon the channel or the transmitted data. They can be pre-computed andstored for use by the domain converter 206, or they can be constructedon the fly to save on storage. In the case that there are only Tnon-zero time domain taps, the complexity reduces to NT².This diagonal decomposition method may also be used to determine otherfrequency-domain tap vectors. The extension to these other taps may bedone by taking other non-principal diagonals of the matricesdiag_(j)(F*_(rx)D_(−i)F_(tx)) and diag_(j)(F*_(rx)D_(N+C−1−i)F_(tx)) anddetermining non-principal-channel estimates according toh ₀ ^(j) =M ₀ ^(j) ĥ _(t)h ₁ ^(j) =M ₁ ^(j) ĥ _(t)

and excluding zero entries in ĥ_(t).

There is further structure in the matrix M₀ that allows furthercomplexity reductions, via use of the Fast Fourier transform. Thissimplification arises by noting thatM ₀ ĥ _(t) =W _(N) g _(t)i.e. the discrete Fourier transform of a vector g_(t), where the vectorg_(t) is obtained as follows.

m₀ = abs(M₀(1, :)) ĥ_(t)^(′) = m₀ ⊙ ĥ_(t)$g_{t} = {{{\hat{h}}_{t}^{\prime}\left( {1\text{:}N} \right)} + \begin{bmatrix}{{\hat{h}}_{t}^{\prime}\left( {N = {{1\text{:}N} + C}} \right)} \\0_{N - {C \times 1}}\end{bmatrix}}$The resulting complexity is approximately N+C+N log N. The vector m₀ haselements which can be pre-computed and stored, or may be computed on thefly to save storage. These elements are given by

$m_{0\; i} = \left\{ \begin{matrix}{1,} & {{i = 1},2,{\ldots\mspace{14mu} C}} \\{\frac{N + C - i}{N},} & {{i = {C + 1}},{{\ldots\mspace{14mu} N} + C}}\end{matrix} \right.$A similar method can be used to obtain h₁ via a Fast Fourier transform.Frequency Domain—Time Domain (Tx Estimate) 214

A time domain estimate of the transmitted symbols is reconstructed fromthe frequency domain version output by decoder 212, on a per OFDM symbolbasis, using the standard inverse FFT method, as found in thetransmitter 100. A cyclic prefix may be added to the symbol, inaccordance with the process undertaken at the transmitter 100.

Equalizer

The equalizer block 210, shown in FIG. 14, is responsible for generatingmetrics (p[i]) suitable for use in the decoder 212. These are typicallya-priori probabilities or log-likelihood ratios. The equaliser 210receives estimates of X fed back from the decoder 212. Another input tothe equaliser 210 is a frequency-domain version of the observation inputy. The equaliser also receives frequency-domain estimates of h₀ and h₁from the domain converter 206 and statistics 318 from the statisticscalculator 308 of channel estimator 204.

An equaliser 210 will attempt to recover all energy pertaining to aparticular symbol of interest while minimising the interference fromother symbols. Recall from eqn 7, that our observation model for eachOFDM symbol isY[i]=R ₀ [i]X[i]+R ₁ [i]X[i−1]+n[i]

We see that observations Y[i] and Y[i+1] have components due to symbolX[i]. This observation model is equivalent to a time-varying, two-tap,vector ISI channel model. The equaliser 210 may obtain decoder inputmetrics from such an observation sequence using many techniques. Theseinclude:

-   -   Trellis based schemes such as APP and Viterbi equalisers. These        may be soft output;    -   Concatenated linear filtering (e.g. LMMSE, MRC etc) following by        soft or hard interference cancellation;    -   Interference Cancellation followed by linear filtering (e.g.        LMMSE).

In one arrangement we treat ICI as noise and obtain the per-subcarrierISI modelY[i]=h ₀ [i]⊙X[i]+h ₁ [i]⊙X[i−1]+z[i]

where the inter-carrier interference (from both symbols i and i−1) hasbeen moved into the noise vector z[i]. Note that the power in the noiseprocess z[i] (σ_(z) ²) is likely to be higher than the power in thesequence n[i] of eqn 7.

Using estimates for h₀[i] and h₁[i] we apply one of the equaliserslisted above. In one preferred embodiment we

-   1. apply a two tap per subcarrier maximum ratio combiner (MRC) for    each received OFDM symbol    {circumflex over (X)} _(mrc) [i]=h ₀ *[i]⊙Y[i]+h ₁ *[i+1]⊙Y[i+1]

where the resulting observation model at the output of the MRC is

X̂_(mrc)[i] = h₀^(*)[i] ⊙ h₀[i] ⊙ X̂[i] + h₁^(*)[i + 1] ⊙ h₁[i + 1] ⊙ X̂[i] + h₁^(*)[i] ⊙ h₀[i] ⊙ X̂[i − 1] + h₁^(*)[i + 1] ⊙ h₀[i + 1] ⊙ X̂[i + 1]h₁^(*)[i + 1] ⊙ h₀[i + 1] ⊙ X̂[i + 1] = α[i] ⊙ X̂[i] + β[i] ⊙ X̂[i − 1] + γ[i] ⊙ X̂[i + 1]

-   -   The interference gain components μ[i] and γ[i] are readily        calculated from the channel estimate sequences {h₀[i]} and        {h₁[i]}

-   2. using previously calculated estimates of X[i−1] and X[i+1] apply    interference cancellation to each OFDM symbol as follows    {circumflex over (X)} _(mrcic) [i]={circumflex over (X)} _(mrc)    [i]−β[i]⊙{circumflex over (X)}[i−1]−γ[i]⊙{circumflex over (X)}[i+1]

-   3. compute decoder metrics from {circumflex over (X)}_(mrcic)[i]    using a Gaussian distribution model with conditional mean equal to    α[i] and estimate of the observation noise power σ_(z) ².

In the case of A receive antennas only the combining at step 2 wouldrequire expansion to include all time and antenna diversity available.

${{\hat{X}}_{mrc}\lbrack i\rbrack} = {{\sum\limits_{a = 1}^{A}\;{{h_{0}^{*}\left\lbrack {i,a} \right\rbrack} \odot {Y\left\lbrack {i,a} \right\rbrack}}} + {{h_{1}^{*}\left\lbrack {{i + 1},a} \right\rbrack} \odot {h_{0}\left\lbrack {{i + 1},a} \right\rbrack}}}$

With the interference coefficients now calculated

${\gamma\lbrack i\rbrack} = {\sum\limits_{a = 1}^{A}\;{{h_{1}^{*}\left\lbrack {{i + 1},a} \right\rbrack} \odot {h_{0}\left\lbrack {{i + 1},a} \right\rbrack}}}$${\beta\lbrack i\rbrack} = {\sum\limits_{a = 1}^{A}\;{{h_{1}^{*}\left\lbrack {i,a} \right\rbrack} \odot {h_{0}\left\lbrack {i,a} \right\rbrack}}}$

In mild delay spread conditions it can be advantageous to use single tapMRC, rather than two tap MRC. Additionally, the equalizer 210 may employthe estimated RMS delay spread of the channel, which is input from thechannel estimator block 204, to adapt its settings accordingly as thechannel conditions alter.

One arrangement for the equaliser 210 is shown in FIG. 15, in which theequaliser 210 includes a linear combiner 402 followed by an interferencecancellation module 404 and a demodulation block 406. The frequencydomain sequence y[i] of OFDM symbol observations is processed by thelinear combiner 402 (being MRC or MMSE in nature).

If a Maximum Ratio Combiner (MRC) is used, the intermediate value outputby the linear combiner 402 is calculated according to:{circumflex over (X)}={circumflex over (R)}*Y

where {circumflex over (R)}=G*_(rx)ĤG_(tx) is an estimate of R,{circumflex over (X)} is an estimate of X and Ĥ is an estimate of thetime varying time domain channel matrix H.

If a Minimum Mean-Squared Error (MMSE) sequence is used, theintermediate value output by the linear combiner 402 is calculated from:{circumflex over (X)}=({circumflex over (R)}*{circumflex over (R)}+σ ²I)⁻¹ {circumflex over (R)}*Y

The output of the combiner 402, (denoted {circumflex over (X)}_(mrc)[i]in the case of an MRC sequence) has interference introduced which isremoved by the following interference cancellation module 404. Theinterference cancellation module forms the “channel” coefficients, γ andβ, internally and using previous (potentially soft) data estimates. TheIC module 404 outputs the improved symbol-estimate sequence {circumflexover (X)}_(mrcic)[i] and a statistic (cp[i]) of the channel coefficientsthat assists the demodulation module 406 when module 406 estimates thestatistics of the channel against which it demodulates. It is preferablethat the demodulator 406 estimates the noise power after the applicationof the interference cancellation module 404 because there may be errorsin the IC process that may contribute to the noise power.

Because the linear combiner 402 and IC stages 404 are linear they may bereversed in order of application.

In other embodiments, the operating method of the combiner 402 may bechanged dynamically between iterations and/or received OFDM symbolindex. In one embodiment the number of received OFDM symbols employed inthe combining process may be set according to a predetermined systemstate metric. In another embodiment the number of frequency domainchannel components employed in the combining process may be setaccording to a predetermined system state metric. In another embodiment,the combiner 402 may use a MMSE technique as described herein for thefirst iteration, and then switch to some other combining technique, e.g.MRC, for subsequent iterations. In another embodiment, the combiner 402may switch from one technique to another, where the techniques may beselected from a lookup table, according to the iteration number, and/orOFDM symbol number. In the previously described embodiments, the systemstate metric may be some function of the RMS delay spread of thechannel.

In other embodiments, the method of equalization may be changeddynamically between iterations and/or received OFDM symbol index. In oneembodiment the number of received OFDM symbols employed in theequalization process is set according to a predetermined system statemetric. In another embodiment, the number of frequency domain channelcomponents employed in the equalization process may be set according toa predetermined system state metric. In the previously describedembodiments, the system state metric may be some function of the RMSdelay spread of the channel.

The per-subcarrier ISI model given in eqn 13 considers a usefulcontribution of the main diagonals of R₀ and R₁, i.e. the vectors h₀ andh₁, while treating other diagonals of these matrices as an effectivecontribution to noise. Recall from the model presented in the early partof this description, that any non-principal diagonal components of R₀and R₁ may also be employed for combining and cancellation. Moreover,the model suggests that a relatively large proportion of the potentialgain from considering additional off-diagonals of R₀ and R₁ may berealized by selecting a small number of such components which resideclose to the principal diagonals. These non-principal diagonals of R₀and R₁ may be provided by the time-to-frequency converter 206, and insuch cases eqn 13 may be generalized to include these inter-carrierinterference terms. These vectors may be included in eqn 13 via transmitsymbol product terms, in a similar manner to h₀ and h₁, while removingtheir contribution to the noise vector z[i]. The methods described abovefor use in the equaliser block 210 may be extended to make use of thisgeneralized model.

Thus, the generalised output of the MRC combiner 402 is:

${{\hat{X}}_{mrc}\lbrack i\rbrack} = {\sum\limits_{j}^{\;}\;{{{\hat{R}}_{j}^{*}\left\lbrack {i + j} \right\rbrack}{Y\left\lbrack {i + j} \right\rbrack}}}$

In the generalised case, the interference cancellation block 404 thengenerates an output:

${{\hat{X}}_{mrcic}\lbrack i\rbrack} = {{{{\hat{R}}_{0}^{*}\lbrack i\rbrack}{{\hat{R}}_{0}\lbrack i\rbrack}{X\lbrack i\rbrack}} - {\sum\limits_{{({j,k})} \neq {({0,0})}}^{\;}\;{{{\hat{R}}_{j}^{*}\left\lbrack {i + j} \right\rbrack}{{\hat{R}}_{k}\left\lbrack {i + j} \right\rbrack}{{\hat{X}}_{mrc}\left\lbrack {i + j - k} \right\rbrack}}}}$

where {{circumflex over (X)}_(mrc)[j], j≠i} equates to estimates forinterfering symbols.

Decoder

The decoder block 212 uses the output of the equalizer 210 as an inputto drive a Forward Error Correcting (FEC) decoder 504, 510. The decoder212 provides an estimate of the transmitted data based upon these inputsand the constraints of the FEC code 104 employed at the transmitter 100.The code constraints may be applied to individual symbols, and/or togroups of symbols, and/or to the entire packet. If an interleaver 106 ispresent in the transmitter 100, then the complementary de-interleavingprocedure 502 is performed at the input of the decoder block 212.

The decoder 212 may use the soft inputs from the equalizer 210, or itmay be a hard input decoder which makes a hard decision at the input.The decoder 212 may employ any soft or hard output algorithm which isapplicable to the FEC code being used. The decoder 510 may, for example,employ the a-posteriori Probability (APP) or Viterbi algorithm, or someform of belief propagation. The outputs of the FEC decoder 504 areInterleaved by interleaver module 506 and provided to symbol mapper 508in order to regenerate an estimate of the transmitted symbols. Mappedsymbols may be either hard or soft values.

FIG. 17 shows an arrangement in which the decoder 212 outputs aninformation sequence estimate only. In this arrangement the input to thedecoder 212 is de-interleaved 502, FEC decoded 510 then FEC encoding 512(as performed in the transmitter 100) is performed prior tointerleaving. The interleaver 514 passes its output to symbol mapper516, which regenerates an estimate of the information sequence to forman output of the decoder 212.

Process Scheduling

In order to reduce receiver side latency, the functional blocksdescribed above may be arranged in a pipeline. Communication betweenblocks may occur on a time slotted basis. A time slot may, for example,be chosen to equal a single OFDM symbol period, some multiple of OFDMsymbol periods, or some division of an OFDM symbol period. For example,the decoder 212 may calculate a new transmitted symbol estimate for agiven time slot while the channel estimator 204 updates an estimatecorresponding to a past time slot.

One or more iterations of the receiver algorithm may be undertaken withthe aim of improving both the transmitted symbol estimate and thechannel estimate. In some cases, a checksum may be present across thedata, or the FEC decoder 504, 510 may have the ability to validate itsoutput. Either may serve as an early stopping criterion for the overalliterative loop.

The code constraint imposed by the particular FEC decoder employed mayplay a role in determining the most efficient pipelined implementation.For example, a coded FEC block may correspond to a single OFDM symbol,as in the case for the 802.16 OFDM standard. Pipelined update schedulessuitable to such cases are described in Applicant's co-pending PCTapplication PCT/AU2006/00120, claiming priority from AustralianProvisional Patent application No 2005904528 filed on 22 Aug. 2005. Insome cases a FEC coding may be performed across multiple OFDM symbols,e.g. as dictated by the 802.11a/g standard. In such cases the receiverlatency can be reduced by running two or more pipelined decoders inparallel. In such a configuration, one decoder is applied to the firstiteration for a given timeslot, the second decoder is applied to thesecond iteration for a past time slot, and so on. The relative delaybetween the selected time slots may be exploited by other pipelinedprocesses, for example in the case of a convolutional code, to performtraceback and calculate new decoder metrics. If required, a decodercontrol schedule may be precomputed and stored in memory for use atruntime. An example schedule which employs two parallel decoders for thecase of an 802.11a/g receiver is discussed in the following section.

Example 802.11a/g Parallel Decoding Schedule

In IEEE 802.11 systems the FEC coding may be unterminated between OFDMsymbols (except for the first data bearing OFDM symbol known as thesignal field). In this example we employ an OFDM symbol based approachin the receiver, for joint data and channel estimation, requiring anOFDM symbol based FEC engine. The main steps in the FEC/ChannelEstimation loop are executed in two parallel streams as shown inTable 1. These streams are executed upon reception of OFDM symbol i fromthe FFT.

TABLE 1 Channel Estimation and FEC Decode OFDM Symbol Threads ThreadsStep Channel Estimation FEC 1 Update i-1, Get i Decode i 2 Temp Updatei, Get i+1 Decode i

These same threads may be employed in a particular embodiment for thecase when each FEC block spans a single OFDM symbol, e.g. as dictated bythe IEEE 802.16-2005 OFDM standard. In this example we must accommodatemulti-OFDM symbol traceback of a Viterbi Trellis, as the 802.11a/g FECis a Convolutional Code.

This may be accomplished under a redefinition of the “Decode i” step. Inthe case of a single OFDM symbol per FEC block, “Decode i” may beinterpreted as the determination of an information bit and Tx Symbolestimate for OFDM symbol i, by decoding OFDM symbol i using a channelestimate for symbol i. In this example, we must construct a Viterbiexecution plan that yields the same outputs but will necessarily requiredifferent inputs to enable traceback.

There are at least three impacts of this traceback requirement

1. The processing of the OFDM symbols must be delayed relative to thereceived symbol wavefront

2. Channel estimates must be provided for all symbols in the Viterbitraceback.

3. A termination plan must be derived for the end of the packet wherethe Trellis is terminated.

The first impact may be addressed by redefining the trigger event forthe processing. In the case of a single OFDM symbol per FEC block we maytrigger on receipt of OFDM Symbol i. In this example case we trigger onreceipt of OFDM Symbol i+D where D is the number of extra OFDM symbolsrequired for quality Viterbi traceback. The constraint length of theConvolutional code is 7 so we would normally require 35 bits oftraceback. For BPSK each OFDM symbol contains only 24 bits so we use twoadditional OFDM symbols, giving a minimum traceback length of 48 bits.It may also be possible to employ a shorter traceback length, with apotential tradeoff of performance for decreased latency. Moreover, forhigher constellation sizes, a traceback length of 35 bits may beprovided with a single additional OFDM symbol.

The second impact may be addressed by redefinition of “Get i”. Whenthere is one OFDM symbol per FEC block this subfunction provides thenecessary channel estimates for the next Decode step. For the 802.11a/gexample, the requirements, in terms of channel estimation, of the decodestep have changed. Not only do we require a channel estimate for symboli, we also require one for OFDM Symbol i+1 and i+2. These are readilyprovided by the Channel Estimate Database (CEDB) using a residual IFOspin method to phase shift the estimate for each forward prediction intime.

The third impact may be addressed by asserting “Packet Decoded” when“Step 2: Decode” traces back from the terminated OFDM symbol (the lastOFDM Symbol) for the first time. This step may be executed uponreception of the last OFDM symbol from the FFT and has the addedadvantage of reducing decode latency.

A schedule, assuming the parallel use of two FEC blocks and one CEDBblock is shown in Table 2.

The set of all packet lengths may be covered by approximately 3 to 4Finite State Machine (FSM) classes. The FSM to be employed for controlcan be determined after the common startup phase, ending after the finaldecoding of the Signal Field in Symbol Interval 6.

The methods described herein advantageously provide a first domainchannel estimation process that employs decoder outcomes to enable thedetermination of an accurate time domain channel estimate.

Channel estimation may be provided where the channel may be time varying(e.g. as induced by mobility).

The frequency domain channel estimates for pairs of successive OFDMsymbols may be used to combine the received OFDM symbols (and acrossantennas if there are multiple antenna).

The frequency domain channel estimates for pairs of successive OFDMsymbols may be used to interference cancel (IC) the combiner output.

The symbol estimates used in the Interference Cancellation are providedby previously decoding the corresponding Received OFDM Symbols.

The previous decoding may be soft output (e.g. Viterbi, APP).

In the described methods a reduced complexity technique (based onpartial correlations) may be employed to determine which taps toestimate and then to use full complexity only on those taps.

It will be appreciated by those skilled in the art, that the inventionis not restricted in its use to this particular application described,neither is the present invention restricted to its preferred embodimentwith regards to the particular elements and/or features described ordepicted herein. It will be appreciated that various modifications canbe made without departing from the principles of the invention.Therefore, the invention should be understood to include all suchmodifications within its scope.

For example, the processing described herein may be generalised to aniterative process involving operations in two different domains. Forexample, the second transform domain may be arise from the use ofLaplacian or Wavelet transforms.

While this invention has been described in connection with specificembodiments thereof, it will be understood that it is capable of furthermodification(s). This application is intended to cover any variationsuses or adaptations of the invention following in general, theprinciples of the invention and comprising such departures from thepresent disclosure as come within known or customary practice within theart to which the invention pertains and as may be applied to theessential features hereinbefore set forth.

As the present invention may be embodied in several forms withoutdeparting from the spirit of the essential characteristics of theinvention, it should be understood that the above described embodimentsare not to limit the present invention unless otherwise specified, butrather should be construed broadly within the spirit and scope of theinvention as defined in the appended claims. Various modifications andequivalent arrangements are intended to be included within the spiritand scope of the invention and appended claims. Therefore, the specificembodiments are to be understood to be illustrative of the many ways inwhich the principles of the present invention may be practiced. In thefollowing claims, means-plus-function clauses are intended to coverstructures as performing the defined function and not only structuralequivalents, but also equivalent structures. For example, although anail and a screw may not be structural equivalents in that a nailemploys a cylindrical surface to secure wooden parts together, whereas ascrew employs a helical surface to secure wooden parts together, in theenvironment of fastening wooden parts, a nail and a screw are equivalentstructures.

“Comprises/comprising” when used in this specification is taken tospecify the presence of stated features, integers, steps or componentsbut does not preclude the presence or addition of one or more otherfeatures, integers, steps, components or groups thereof.”

REFERENCES

-   [1] P. Alexander and D. Haley, “Physical Layer Challenges in Mobile    Broadband OFDM Communications Systems,” Cohda Wireless Whitepaper,    August 2005.-   [2] J. Medbo and P. Schramm “Channel Models for HIPERLAN/2 in    Different Indoor Scenarios,” ETSI/BRAN document no. 3ER1085B, March    1998.-   [3] J. Heiskala and J. Terry, “OFDM Wireless LANs: A Theoretical and    Practical Guide”, Sams Publishing, 2002.-   [4] IEEE Working Group 802.20, “Channel models for IEEE 802.20 MBWA    system simulations Rev 03,” November 2003.-   [5] IEEE 802.11 WG, “IEEE Std 802.11a-1999(R2003), Part 11: Wireless    LAN Medium Access Control (MAC) and Physical Layer (PHY)    specifications, High-speed Physical Layer in the 5 GHz Band.”-   [6] IEEE 802.11 WG, “IEEE Std 802.11g-2003, Part 11: Wireless LAN    Medium Access Control (MAC) and Physical Layer (PHY) specifications,    Amendment 4: Further Higher Data Rate Extension in the 2.4 GHz    Band.”-   [7] IEEE 802.16 WG, “IEEE Std 802.16-2005, Part 16: Air Interface    for Fixed Broadband Wireless Access Systems.”

What is claimed is:
 1. A method of data processing in a multi-carrierwireless communication network, the method comprising: obtaining a modelof a channel in the multi-carrier wireless communication network;receiving a multi-carrier signal transmitted over the channel, themulti-carrier signal comprising a sequence of encoded symbols carried ona plurality of sub-carriers, wherein an error control coding of theencoded symbols occurs over a plurality of symbols; estimating, based onthe received multi-carrier signal, parameters of the model of thechannel; determining a decoded symbol based on a determination ofestimates of two or more of the sequence of encoded symbols from thereceived multi-carrier signal, said determination of symbol estimatesincluding: using at least one of the estimated parameters; and combiningobservations of at least two successive encoded symbols in the sequenceof encoded symbols; and updating the estimated parameters duringreception of the multi-carrier signal using at least one of theestimates of the two or more of the sequence of symbols.
 2. The methodas claimed in claim 1 further comprising: iterating said updating anddetermining steps, wherein the determined decoded symbol is used toupdate the estimated model parameters.
 3. The method as claimed in claim2 wherein said iterating continues until at least one stopping criterionis satisfied, the stopping criteria comprising at least one of:completion of a maximum number of iterations; reporting of a validsymbol decoding; reporting of a soft belief of a valid symbol decoding,the soft belief exceeding a threshold value; and successful performanceof a checksum across decoded symbols.
 4. The method as claimed in claim2 wherein said updating of estimated parameters comprises: modifying aset of accumulators using at least one data packet symbol of thereceived signal and at least one training symbol.
 5. The method asclaimed in claim 4 wherein the at least one training symbol comprises apreviously-determined estimate of a transmitted symbol.
 6. The method asclaimed in claim 4 to wherein said updating calculates updatedparameters using at least one of: a sliding correlator; a linear MMSEfilter; and a zero forcing (ZF) filter.
 7. The method as claimed claim 4wherein said updating comprises performing a sliding correlation of atleast a portion of the received signal with a time domain estimate of atransmitted symbol.
 8. The method as claimed in claim 2 comprising:generating metrics for use by a decoder that is complementary to anencoder used in transmitting the signal, wherein the metrics aregenerated dependent on the estimated parameters and estimated symbols.9. The method as claimed in claim 7 wherein said updating step applies adecorrelating filter to an output of the sliding correlation.
 10. Themethod as claimed in claim 1 wherein said determination of symbolestimates is performed in a second domain, the method comprising:transforming at least one of the estimated parameters from a time domainto provide at least one transformed parameter in the second domain. 11.The method as claimed in claim 10 wherein the second domain is afrequency domain.
 12. The method as claimed in claim 10 wherein saidcombining uses the at least one transformed parameter.
 13. The method asclaimed in claim 10 wherein, in estimating an i^(th) symbol, saiddetermining step comprises: calculating interference from at least oneadjacent symbol in the sequence using the at least one transformedparameter; and cancelling the calculated interference from the estimateof the i^(th) symbol.
 14. The method as claimed in claim 11 wherein saidtransforming comprises calculating transformed parameters h₀ and h₁using:h ₀=(Z ₁ ⊙Z ₂)l _(f)h ₁=(Z ₁ ⊙Z ₁)u _(f) where the bracketed terms are constant and l_(f)and u_(f) are Fourier transforms of vectors defining circulant matricesderived from the time-domain parameters.
 15. The method as claimed inclaim 11 wherein said transforming comprises calculating transformedparameters h₀ and h₁ using:h ₀ =M ₀ ĥ _(t)h ₁ =M ₁ ĥ _(t) Where M₀ is a matrix whose column number i is the vectorformed from diag(F*_(rx)D_(−i)F_(tx)) and M₁ is a matrix whose columnnumber i is the vector formed from diag(F*_(rx)D_(N+C−1−i)F_(tx)), D_(i)is a matrix that is zero in every element except along diagonal i, andone in every element along diagonal i, where i=0 denotes the principaldiagonal and negative values of i indicate diagonals below the principaldiagonal, the M matrices being independent of the channel andtransmitted data and ĥ_(t) is the time-domain estimate of the channel.16. The method as claimed in claim 13 wherein said transforming stepprovides a matrix representation of the model of the channel and whereinsaid combining step and said cancelling step use only principal diagonalcomponents of the matrix representation.
 17. The method as claimed inclaim 13 wherein said determining step comprises estimating the i^(th)symbol using forward-error-correction (FEC) decoding and said updatingstep comprises updating the estimated parameters using the estimatedi^(th) symbol.
 18. The method as claimed in claim 14 wherein saidtransforming comprises forming a linear combination of elements of theestimated channel model.
 19. The method as claimed in claim 12 whereinsaid combining step uses at least one of: a maximum ratio combiner(MRC); and a minimum mean squared error (MMSE) combiner.
 20. The methodas claimed in claim 18 wherein said forming a linear combination uses aknown structure of zero-valued elements in the estimated channel model.21. The method as claimed in claim 19 comprising changing a techniqueused in said combining step.
 22. The method as claimed in claim 19comprising changing a number of received symbols used in said combiningstep.
 23. The method as claimed in claim 22 wherein the number ischanged dependent on a system state metric.
 24. The method according toclaim 1 wherein the signal comprises a known portion and said estimationuses the known portion to estimate an initial estimate of the modelparameters.
 25. The method as claimed in claim 24 wherein saidestimating comprises performing a sliding correlation of at least aportion of the received signal with the known portion available at areceiver in the communication network.
 26. The method as claimed inclaim 24 wherein said estimating step uses a minimum mean squared errorscaling factor corresponding to a noise component of the receivedobservation.
 27. The method as claimed in claim 24 wherein the knownportion comprises repeated symbols having a set of zero-valuedsubcarriers in a frequency domain.
 28. The method as claimed in claim 25wherein said estimating comprises applying a decorrelating filter to anoutput of the sliding correlation.
 29. The method as claimed in claim 27further comprising: calculating an estimate of received noise power froma variance of received values at subcarriers corresponding to thezero-valued subcarriers.
 30. The method as claimed in claim 1 whereinsaid updating of estimated parameters is performed in a time domain. 31.The method as claimed in claim 1 wherein said updating of estimatedparameters is performed in a frequency domain.
 32. The method as claimedin claim 31 wherein said updating of estimated parameters comprisesdivision or conjugate multiplication of a received frequency domainobservation by an estimate of the transmitted sequence in the frequencydomain.
 33. The method as claimed in claim 1 wherein said updatingchanges from the a domain to a frequency domain after a point in timeduring reception of the signal.
 34. The method as claimed in claim 33wherein the point in time is selected from the group consisting of: apredetermined delay after a start of said reception of the signal; atime dynamically selected dependent on a system state metric; a timedynamically selected dependent on statistic of the estimated channelmodel; and a time dynamically selected dependent on an estimated channeldelay spread.
 35. The method as claimed in claim 1 wherein the model isgiven byY[i]=R ₀ [i]X[i]+R ₁ [i]X[i−1]+n[i] where bracketed terms indicate asymbol period index; R₀ and R₁ are submatrices of a matrix R thatgenerally defines a channel transfer function given byR=G* _(rx) HG _(tx) where G*_(rx) is a receive-side transform matrixwhich translates received time-domain multicarrier symbols into afrequency domain; H is a time-domain linear-convolution matrix used tomodel a linear convolution of the transmitted signal with the channel,and; G_(tx) is a transmit-side transform matrix, which translatestransmitted multicarrier symbols into a time domain; Y is a vector in afrequency domain representing the L multicarrier symbols received insaid receiving step; X is a vector in a frequency domain representing Ltransmitted symbols; and n is additive white Gaussian noise.
 36. Themethod as claimed in claim 1 wherein the model used for said estimatingand updating is given by:${Y\lbrack i\rbrack} = {{\sum\limits_{j}^{\;}\;{{R_{j}\lbrack i\rbrack}{X\left\lbrack {i - j} \right\rbrack}}} + {{n\lbrack i\rbrack}.}}$37. The method as claimed in claim 1 wherein the model used for saidestimating and updating is adapted to utilise OFDM symbol constructions.38. The method as claimed in claim 1 further comprising: determining anestimate of the average power of the channel from the estimated modelparameters; and determining an estimate of a RMS delay spread.
 39. Themethod as claimed in claim 1, comprising: selecting a set of taps forfurther processing based on the estimated model parameters, saidselection comprising: comparing a signal parameter for a plurality oftaps to a predetermined threshold, the signal parameter being derivedfrom the estimated model parameters; and providing signal processing toonly those taps that are determined to have a signal parameter thatexceeds the threshold.
 40. The method as claimed in claim 39 wherein thepredetermined threshold comprises one of: a fixed value; a valuedetermined by a function of the estimated model parameters; a variablevalue corresponding to a function of the estimate determined at eachupdate of the estimate.
 41. The method as claimed in claim 39 furthercomprising: for each tap with a signal parameter exceeding thethreshold, providing signal processing to a group of taps disposed oneither side of the tap.
 42. The method as claimed in claim 39 comprisinglocking the selection of taps for processing.
 43. The method as claimedin claim 40 wherein the threshold comprises a value corresponding to:the power of the channel estimate; a function of the power of thechannel estimate.
 44. The method as claimed in claim 41 wherein the sizeof the group is determined as one of: a fixed number; a function of aRMS delay spread statistic.
 45. The method as claimed in claim 42wherein a time for locking the tap selection is one of: a predeterminedtime; a lock time obtained from a look-up table dependent on a metric ofthe estimated channel model.
 46. The method as claimed in claim 45wherein the metric is an estimate of noise power in the received signal.47. The method as claimed in claim 1 wherein said updating comprises:applying a filter to a current estimate of the parameters and at leastone previous estimate.
 48. The method as claimed in claim 47 wherein thefilter is an auto-regression filter.
 49. The method as claimed in claim1 wherein said estimating accumulates values in an accumulatorcorresponding to each tap used for the estimated model, and wherein saidupdating uses the accumulated values.
 50. The method as claimed in claim1 wherein said estimating of model parameters is performed in a timedomain.
 51. A computer program product comprising machine-readableprogram code recorded on a non-transitory machine-readable recordingmedium, for controlling the operation of a data processing apparatus onwhich the program code executes to perform a method of data processingin a multi-carrier wireless communication network, the methodcomprising: obtaining a model of a channel in the multi-carrier wirelesscommunication network; receiving a multi-carrier signal transmitted overthe channel, the multi-carrier signal comprising a sequence of encodedsymbols carried on a plurality of sub-carriers, wherein an error controlcoding of the encoded symbols occurs over a plurality of symbols;estimating, based on the received signal, parameters of the model of thechannel; determining a decoded symbol based on a determination ofestimates of two or more of the sequence of encoded symbols from thereceived multi-carrier signal, said determination of symbol estimatesincluding: using at least one of the estimated model parameters;combining observations of at least two successive symbols in thesequence of symbols; and updating the estimated model parameters duringreception of the multi-carrier signal using at least one of theestimates of the two or more of the sequence of symbols.
 52. A computerprogram product comprising machine-readable program code recorded on anon-transitory machine-readable recording medium, for controlling theoperation of a data processing apparatus on which the program codeexecutes to perform a method of data processing in a multi-carrierwireless communication network, the method comprising: obtaining a modelof a channel in the multi-carrier wireless communication network;receiving a multi-carrier signal transmitted over the channel, themulti-carrier signal comprising a sequence of symbols carried on aplurality of sub-carriers; estimating, based on the received signal,parameters of the model of the channel; transforming at least one of theestimated parameters from a time domain to provide at least onetransformed parameter in a second domain; determining a decoded symbolbased on a determination of estimates of two or more of the sequence ofencoded symbols from the received multi-carrier signal, saiddetermination of symbol estimates including: using the at least onetransformed parameter; combining observations of at least two successivesymbols in the sequence of symbols; and updating the estimated modelparameters during reception of the multi-carrier signal using at leastone of the estimates of the two or more of the sequence of symbols.